Machine Learning in 2D Materials Science by Parvathi Chundi (.PDF)

File Size: 27.7 MB

Machine Learning in 2D Materials Science by Parvathi Chundi, Venkataramana Gadhamshetty, Bharat K. Jasthi, Carol Lushbough
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Overview: Data Science and Machine Learning (ML) methods are increasingly being used to transform the way research is being conducted in materials science to enable new discoveries and design new materials. For any materials science researcher or student, it may be daunting to figure out if ML techniques are useful for them or, if so, which ones are applicable in their individual contexts, and how to study the effectiveness of these methods systematically. ML has evolved as a subfield of Artificial Intelligence (AI), learning from the data collected historically or from experiments, and using it for future actions. In general, ML models consider the patterns of the input and adjusts internal structures to approximate the relationship between input and output. ML is also used to identify hidden patterns of data distributions to come up with meaningful relationships. The ability to learn unforeseen relationships from data without depending on explicitly programmed prior guidance is one of the main reasons why there are a plethora of ML-based applications. The very early definition for ML, “Field of study that gives computers the ability to learn without being explicitly programmed” is still valid.
Genre: Non-Fiction > Tech & Devices

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